skill-optimizer

To evaluate and improve AI agent skills, apply an 8-dimension rubric for static and dynamic analysis on SKILL.md files, scoring both structural integrity and functional performance to identify areas for optimization.

Hill-climbing optimization for agent development is an iterative process that uses git-based version control to test, validate, and retain only those modifications that measurably increase AI skill performance.

Yes, you can autonomously test and validate changes to SKILL.md files through automated testing and sub-agent verification, ensuring that only successful updates are kept within the version control loop.

Yes, git version control is required for agent skill meta-learning to maintain quality control, track iterative experiments, and manage the retention or rejection of proposed performance improvements.

Human-in-the-loop validation provides clear checkpoints for user confirmation during the autonomous optimization loop, ensuring that all structural and functional changes align with your specific operational requirements.

Autonomous skill evaluation requires human-in-the-loop checkpoints to prevent misaligned modifications, meaning the optimization loop cannot fully run without user confirmation at critical validation stages before finalizing updates.